Radiomics is an active area of research in medical image analysis, the low reproducibility of radiomics has limited its applicability to clinical practice. This issue is especially prominent when radiomic features are calculated from noisy images, such as low dose computed tomography (CT) scans. In this article, we investigate the possibility of improving the reproducibility of radiomic features calculated on noisy CTs by using generative models for denoising.One traditional denoising method - non-local means - and two generative models - encoder-decoder networks (EDN) and conditional generative adversarial networks (CGANs) - were selected as the test models. We added noise to the sinograms of full dose CTs to mimic low dose CTs with two different levels of noise: low-noise CT and high-noise CT. Models were trained on high-noise CTs and used to denoise low-noise CTs without re-training. We also test the performance of our model in real data, using dataset of same-day repeat low dose CTs to assess the reproducibility of radiomic features in denoised images. The EDN and the CGAN improved the concordance correlation coefficients (CCC) of radiomic features for low-noise images from 0.87 to 0.92 and for high-noise images from 0.68 to 0.92 respectively. Moreover, the EDN and the CGAN improved the test-retest reliability of radiomic features (mean CCC increased from 0.89 to 0.94) based on real low dose CTs. The results show that denoising using EDN and CGANs can improve the reproducibility of radiomic features calculated on noisy CTs. Moreover, images with different noise levels can be denoised to improve the reproducibility using these models without re-training, as long as the noise intensity is equal or lower than that in high-noise CTs. To the authors' knowledge, this is the first effort to improve the reproducibility of radiomic features calculated on low dose CT scans.
翻译:辐射感应是医学图像分析的一个积极研究领域, 放射感应的低可复制性限制了对临床实践的可应用性。 当从噪音图像中计算出放射特征时, 这个问题特别突出, 比如低剂量计算断声仪扫描。 在本篇文章中, 我们研究是否可能通过使用基因化模型来改善在噪音CT上计算出的放射特征的可复制性。 一种传统脱色方法 - 非局部手段 - 和两种基因化模型 - 摄氏网络( EDN) 和有条件的血清对抗网络 (CGANs) 被选为测试模型。 我们把噪音加在全剂量断层的感应光镜上, 低噪音CTs和高音调CTs。 模型被训练到高音化的CTs, 并且用来在没有再训练的情况下降低这些低噪音感应变力。 我们还在真实数据中测试我们模型的性能, 使用连续重复低剂量的C9CNS值测试结果, 用来评估DNA的可更新性能。